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Fachgebiet Wissensverarbeitung (KDE), EECS, Universität Kassel
„So wie sich Landkarten für die Navigation in Landschaften etabliert haben, untersuchen wir den Einsatz diskreter und algebraischer Strukturen für die Navigation in Wissenslandschaften.“
Das Fachgebiet Wissensverarbeitung des Fachbereichs Elektrotechnik/Informatik forscht an der Entwicklung von Methoden zur Wissensentdeckung und Wissensrepräsentation (Approximation und Exploration von Wissen, Ordnungsstrukturen in Wissen, Ontologieentwicklung) in Daten als auch in der Analyse von (sozialen) Netzwerkdaten und damit verbundenen Wissensprozessen (Metriken in Netzwerken, Anomalieerkennung, Charakterisierung von sozialen Netzwerken). Dabei liegt ein Schwerpunkt auf der exakten algebraischen Modellierung der verwendeten Strukturen und auf der Evaluierung und Neuentwicklung von Netzwerkmaßen. Neben der Erforschung von Grundlagen in den Gebieten Ordnungs- und Verbandstheorie, Beschreibungslogiken, Graphentheorie und Ontologie werden auch Anwendungen – bspw. in sozialen Medien sowie in der Szientometrie – erforscht.
Das Fachgebiet Wissensverarbeitung ist Mitglied im Wissenschaftlichen Zentrum für Informationstechnik-Gestaltung (ITeG) der Universität Kassel, im Wissenschaftlichen Zentrum INCHER der Universität Kassel und im Forschungszentrum L3S.
Testen Sie unser Social-Bookmark-System BibSonomy sowie unsere Namens-Suchmaschine Nameling!Unsere neusten Publikationen
- URLBibTeXEndNote1.Schäfermeier, B., Hirth, J., Hanika, T.: Research Topic Flows in Co-Authorship Networks, http://arxiv.org/abs/2206.07980, (2022).In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.
@misc{schafermeier2022research,
abstract = {In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.},
author = {Schäfermeier, Bastian and Hirth, Johannes and Hanika, Tom},
keywords = {co-authorships myown scientometrics topic-flows topic-models},
note = {cite arxiv:2206.07980Comment: 28 pages},
title = {Research Topic Flows in Co-Authorship Networks},
year = 2022
}%0 Generic
%1 schafermeier2022research
%A Schäfermeier, Bastian
%A Hirth, Johannes
%A Hanika, Tom
%D 2022
%T Research Topic Flows in Co-Authorship Networks
%U http://arxiv.org/abs/2206.07980
%X In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields. - URLBibTeXEndNoteDOI1.Schäfermeier, B., Stumme, G., Hanika, T.: Mapping Research Trajectories, https://arxiv.org/abs/2204.11859, (2022).
@misc{https://doi.org/10.48550/arxiv.2204.11859,
author = {Schäfermeier, Bastian and Stumme, Gerd and Hanika, Tom},
keywords = {myown research_trajectories trajectory_mapping},
publisher = {arXiv},
title = {Mapping Research Trajectories},
year = 2022
}%0 Generic
%1 https://doi.org/10.48550/arxiv.2204.11859
%A Schäfermeier, Bastian
%A Stumme, Gerd
%A Hanika, Tom
%D 2022
%I arXiv
%R 10.48550/ARXIV.2204.11859
%T Mapping Research Trajectories
%U https://arxiv.org/abs/2204.11859 - URLBibTeXEndNoteDOI1.Hanika, T., Schneider, F.M., Stumme, G.: Intrinsic dimension of geometric data sets. Tohoku Mathematical Journal. 74, 23–52 (2022).The curse of dimensionality is a phenomenon frequently observed in machine learning (ML) and knowledge discovery (KD). There is a large body of literature investigating its origin and impact, using methods from mathematics as well as from computer science. Among the mathematical insights into data dimensionality, there is an intimate link between the dimension curse and the phenomenon of measure concentration, which makes the former accessible to methods of geometric analysis. The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we define a concept of geometric data set and introduce a metric as well as a partial order on the set of isomorphism classes of such data sets. Based on these objects, we propose and investigate an axiomatic approach to the intrinsic dimension of geometric data sets and establish a concrete dimension function with the desired properties. Our model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments.
@article{10.2748/tmj.20201015a,
abstract = {The curse of dimensionality is a phenomenon frequently observed in machine learning (ML) and knowledge discovery (KD). There is a large body of literature investigating its origin and impact, using methods from mathematics as well as from computer science. Among the mathematical insights into data dimensionality, there is an intimate link between the dimension curse and the phenomenon of measure concentration, which makes the former accessible to methods of geometric analysis. The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we define a concept of geometric data set and introduce a metric as well as a partial order on the set of isomorphism classes of such data sets. Based on these objects, we propose and investigate an axiomatic approach to the intrinsic dimension of geometric data sets and establish a concrete dimension function with the desired properties. Our model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments.},
author = {Hanika, Tom and Schneider, Friedrich Martin and Stumme, Gerd},
journal = {Tohoku Mathematical Journal},
keywords = {dcd fca intrinsic itegpub kde kdepub loewe myown publist},
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pages = {23 -- 52},
publisher = {Tohoku University, Mathematical Institute},
title = {Intrinsic dimension of geometric data sets},
volume = 74,
year = 2022
}%0 Journal Article
%1 10.2748/tmj.20201015a
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%A Schneider, Friedrich Martin
%A Stumme, Gerd
%D 2022
%I Tohoku University, Mathematical Institute
%J Tohoku Mathematical Journal
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%R 10.2748/tmj.20201015a
%T Intrinsic dimension of geometric data sets
%U https://doi.org/10.2748/tmj.20201015a
%V 74
%X The curse of dimensionality is a phenomenon frequently observed in machine learning (ML) and knowledge discovery (KD). There is a large body of literature investigating its origin and impact, using methods from mathematics as well as from computer science. Among the mathematical insights into data dimensionality, there is an intimate link between the dimension curse and the phenomenon of measure concentration, which makes the former accessible to methods of geometric analysis. The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we define a concept of geometric data set and introduce a metric as well as a partial order on the set of isomorphism classes of such data sets. Based on these objects, we propose and investigate an axiomatic approach to the intrinsic dimension of geometric data sets and establish a concrete dimension function with the desired properties. Our model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments. - URLBibTeXEndNoteDOI1.Hanika, T., Hirth, J.: Knowledge cores in large formal contexts. Annals of Mathematics and Artificial Intelligence. (2022).Knowledge computation tasks, such as computing a base of valid implications, are often infeasible for large data sets. This is in particular true when deriving canonical bases in formal concept analysis (FCA). Therefore, it is necessary to find techniques that on the one hand reduce the data set size, but on the other hand preserve enough structure to extract useful knowledge. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and (maybe) interesting patterns. An essentially different approach is used in network science, called k-cores. These cores are able to reflect rare patterns, as long as they are well connected within the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence of bi-partite graphs and formal contexts. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts.
@article{Hanika2022,
abstract = {Knowledge computation tasks, such as computing a base of valid implications, are often infeasible for large data sets. This is in particular true when deriving canonical bases in formal concept analysis (FCA). Therefore, it is necessary to find techniques that on the one hand reduce the data set size, but on the other hand preserve enough structure to extract useful knowledge. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and (maybe) interesting patterns. An essentially different approach is used in network science, called k-cores. These cores are able to reflect rare patterns, as long as they are well connected within the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence of bi-partite graphs and formal contexts. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts.},
author = {Hanika, Tom and Hirth, Johannes},
journal = {Annals of Mathematics and Artificial Intelligence},
keywords = {2022 bigdata bipartite cores fca itegpub k-cores kde kdepub myown publist},
month = {apr},
title = {Knowledge cores in large formal contexts},
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}%0 Journal Article
%1 Hanika2022
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%A Hirth, Johannes
%D 2022
%J Annals of Mathematics and Artificial Intelligence
%R 10.1007/s10472-022-09790-6
%T Knowledge cores in large formal contexts
%U https://doi.org/10.1007/s10472-022-09790-6
%X Knowledge computation tasks, such as computing a base of valid implications, are often infeasible for large data sets. This is in particular true when deriving canonical bases in formal concept analysis (FCA). Therefore, it is necessary to find techniques that on the one hand reduce the data set size, but on the other hand preserve enough structure to extract useful knowledge. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and (maybe) interesting patterns. An essentially different approach is used in network science, called k-cores. These cores are able to reflect rare patterns, as long as they are well connected within the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence of bi-partite graphs and formal contexts. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts. - URLBibTeXEndNoteDOI1.Hanika, T., Hirth, J.: Exploring Scale-Measures of Data Sets. In: Braud, A., Buzmakov, A., Hanika, T., and Ber, F.L. (eds.) Formal Concept Analysis - 16th International Conference, ICFCA 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings. pp. 261–269. Springer (2021).
@inproceedings{DBLP:conf/icfca/HanikaH21,
author = {Hanika, Tom and Hirth, Johannes},
booktitle = {Formal Concept Analysis - 16th International Conference, ICFCA 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings},
editor = {Braud, Agnès and Buzmakov, Aleksey and Hanika, Tom and Ber, Florence Le},
keywords = {closure closure-system exploring fca lattice myown scaling},
pages = {261--269},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
title = {Exploring Scale-Measures of Data Sets},
volume = 12733,
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}%0 Conference Paper
%1 DBLP:conf/icfca/HanikaH21
%A Hanika, Tom
%A Hirth, Johannes
%B Formal Concept Analysis - 16th International Conference, ICFCA 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings
%D 2021
%E Braud, Agnès
%E Buzmakov, Aleksey
%E Hanika, Tom
%E Ber, Florence Le
%I Springer
%P 261--269
%R 10.1007/978-3-030-77867-5_17
%T Exploring Scale-Measures of Data Sets
%U https://doi.org/10.1007/978-3-030-77867-5_17
%V 12733 - BibTeXEndNoteDOI1.Felde, M., Stumme, G.: Triadic Exploration and Exploration with Multiple Experts. In: Braud, A., Buzmakov, A., Hanika, T., and Le Ber, F. (eds.) Formal Concept Analysis. pp. 175–191. Springer International Publishing, Cham (2021).Formal Concept Analysis (FCA) provides a method called attribute exploration which helps a domain expert discover structural dependencies in knowledge domains that can be represented by a formal context (a cross table of objects and attributes). Triadic Concept Analysis is an extension of FCA that incorporates the notion of conditions. Many extensions and variants of attribute exploration have been studied but only few attempts at incorporating multiple experts have been made. In this paper we present triadic exploration based on Triadic Concept Analysis to explore conditional attribute implications in a triadic domain. We then adapt this approach to formulate attribute exploration with multiple experts that have different views on a domain.
@inproceedings{10.1007/978-3-030-77867-5_11,
abstract = {Formal Concept Analysis (FCA) provides a method called attribute exploration which helps a domain expert discover structural dependencies in knowledge domains that can be represented by a formal context (a cross table of objects and attributes). Triadic Concept Analysis is an extension of FCA that incorporates the notion of conditions. Many extensions and variants of attribute exploration have been studied but only few attempts at incorporating multiple experts have been made. In this paper we present triadic exploration based on Triadic Concept Analysis to explore conditional attribute implications in a triadic domain. We then adapt this approach to formulate attribute exploration with multiple experts that have different views on a domain.},
address = {Cham},
author = {Felde, Maximilian and Stumme, Gerd},
booktitle = {Formal Concept Analysis},
editor = {Braud, Agnès and Buzmakov, Aleksey and Hanika, Tom and Le Ber, Florence},
keywords = {2021 attribute-exploration fca itegpub myown},
pages = {175--191},
publisher = {Springer International Publishing},
title = {Triadic Exploration and Exploration with Multiple Experts},
year = 2021
}%0 Conference Paper
%1 10.1007/978-3-030-77867-5_11
%A Felde, Maximilian
%A Stumme, Gerd
%B Formal Concept Analysis
%C Cham
%D 2021
%E Braud, Agnès
%E Buzmakov, Aleksey
%E Hanika, Tom
%E Le Ber, Florence
%I Springer International Publishing
%P 175--191
%R 10.1007/978-3-030-77867-5_11
%T Triadic Exploration and Exploration with Multiple Experts
%X Formal Concept Analysis (FCA) provides a method called attribute exploration which helps a domain expert discover structural dependencies in knowledge domains that can be represented by a formal context (a cross table of objects and attributes). Triadic Concept Analysis is an extension of FCA that incorporates the notion of conditions. Many extensions and variants of attribute exploration have been studied but only few attempts at incorporating multiple experts have been made. In this paper we present triadic exploration based on Triadic Concept Analysis to explore conditional attribute implications in a triadic domain. We then adapt this approach to formulate attribute exploration with multiple experts that have different views on a domain.
%@ 978-3-030-77867-5 - URLBibTeXEndNoteDOI1.Schaefermeier, B., Stumme, G., Hanika, T.: Topic space trajectories. Scientometrics. 126, 5759–5795 (2021).The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present topic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. In addition to a thorough introduction of our method, our focus is on an extensive analysis of the results we achieved. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work. An advantage in these applications over previous methods lies in the good interpretability of the results obtained through our methods.
@article{Schaefermeier2021,
abstract = {The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present topic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. In addition to a thorough introduction of our method, our focus is on an extensive analysis of the results we achieved. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work. An advantage in these applications over previous methods lies in the good interpretability of the results obtained through our methods.},
author = {Schaefermeier, Bastian and Stumme, Gerd and Hanika, Tom},
journal = {Scientometrics},
keywords = {2021 itegpub l3spub learning machine myown space topic},
month = {may},
number = 7,
pages = {5759–5795},
title = {Topic space trajectories},
volume = 126,
year = 2021
}%0 Journal Article
%1 Schaefermeier2021
%A Schaefermeier, Bastian
%A Stumme, Gerd
%A Hanika, Tom
%D 2021
%J Scientometrics
%N 7
%P 5759–5795
%R 10.1007/s11192-021-03931-0
%T Topic space trajectories
%U https://doi.org/10.1007/s11192-021-03931-0
%V 126
%X The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present topic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. In addition to a thorough introduction of our method, our focus is on an extensive analysis of the results we achieved. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work. An advantage in these applications over previous methods lies in the good interpretability of the results obtained through our methods. - URLBibTeXEndNote1.Dürrschnabel, D., Stumme, G.: Force-Directed Layout of Order Diagrams using Dimensional Reduction, http://arxiv.org/abs/2102.02684, (2021).Order diagrams allow human analysts to understand and analyze structural properties of ordered data. While an experienced expert can create easily readable order diagrams, the automatic generation of those remains a hard task. In this work, we adapt force-directed approaches, which are known to generate aesthetically-pleasing drawings of graphs, to the realm of order diagrams. Our algorithm ReDraw thereby embeds the order in a high dimension and then iteratively reduces the dimension until a two-dimensional drawing is achieved. To improve aesthetics, this reduction is equipped with two force-directed steps where one optimizes on distances of nodes and the other on distances of lines in order to satisfy a set of a priori fixed conditions. By respecting an invariant about the vertical position of the elements in each step of our algorithm we ensure that the resulting drawings satisfy all necessary properties of order diagrams. Finally, we present the results of a user study to demonstrate that our algorithm outperforms comparable approaches on drawings of lattices with a high degree of distributivity.
@misc{durrschnabel2021forcedirected,
abstract = {Order diagrams allow human analysts to understand and analyze structural properties of ordered data. While an experienced expert can create easily readable order diagrams, the automatic generation of those remains a hard task. In this work, we adapt force-directed approaches, which are known to generate aesthetically-pleasing drawings of graphs, to the realm of order diagrams. Our algorithm ReDraw thereby embeds the order in a high dimension and then iteratively reduces the dimension until a two-dimensional drawing is achieved. To improve aesthetics, this reduction is equipped with two force-directed steps where one optimizes on distances of nodes and the other on distances of lines in order to satisfy a set of a priori fixed conditions. By respecting an invariant about the vertical position of the elements in each step of our algorithm we ensure that the resulting drawings satisfy all necessary properties of order diagrams. Finally, we present the results of a user study to demonstrate that our algorithm outperforms comparable approaches on drawings of lattices with a high degree of distributivity.},
author = {Dürrschnabel, Dominik and Stumme, Gerd},
keywords = {2021 Dimensional_Reduction Force-Directed_Algorithms Graph_Drawing Lattice_Drawing Order_Diagram_Drawing Ordered_Sets myown},
note = {cite arxiv:2102.02684Comment: 16 pages, 6 figures, 4 algorithms, for source code refer to https://github.com/domduerr/redraw},
title = {Force-Directed Layout of Order Diagrams using Dimensional Reduction},
year = 2021
}%0 Generic
%1 durrschnabel2021forcedirected
%A Dürrschnabel, Dominik
%A Stumme, Gerd
%D 2021
%T Force-Directed Layout of Order Diagrams using Dimensional Reduction
%U http://arxiv.org/abs/2102.02684
%X Order diagrams allow human analysts to understand and analyze structural properties of ordered data. While an experienced expert can create easily readable order diagrams, the automatic generation of those remains a hard task. In this work, we adapt force-directed approaches, which are known to generate aesthetically-pleasing drawings of graphs, to the realm of order diagrams. Our algorithm ReDraw thereby embeds the order in a high dimension and then iteratively reduces the dimension until a two-dimensional drawing is achieved. To improve aesthetics, this reduction is equipped with two force-directed steps where one optimizes on distances of nodes and the other on distances of lines in order to satisfy a set of a priori fixed conditions. By respecting an invariant about the vertical position of the elements in each step of our algorithm we ensure that the resulting drawings satisfy all necessary properties of order diagrams. Finally, we present the results of a user study to demonstrate that our algorithm outperforms comparable approaches on drawings of lattices with a high degree of distributivity. - BibTeXEndNote1.Stubbemann, M., Stumme, G.: The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks. arXiv preprint arXiv:2110.13774. (2021).
@article{stubbemann2021mont,
author = {Stubbemann, Maximilian and Stumme, Gerd},
journal = {arXiv preprint arXiv:2110.13774},
keywords = {2021 itegpub l3spub myown regio},
title = {The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks},
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%J arXiv preprint arXiv:2110.13774
%T The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks - URLBibTeXEndNoteDOI1.Stubbemann, L., Dürrschnabel, D., Refflinghaus, R.: Neural Networks for Semantic Gaze Analysis in XR Settings. ACM Symposium on Eye Tracking Research and Applications. ACM (2021).
@inproceedings{Stubbemann_2021,
author = {Stubbemann, Lena and Dürrschnabel, Dominik and Refflinghaus, Robert},
booktitle = {ACM Symposium on Eye Tracking Research and Applications},
keywords = {2021 gaze_analysis itegpub myown neural_networks},
month = {may},
publisher = {ACM},
title = {Neural Networks for Semantic Gaze Analysis in XR Settings},
year = 2021
}%0 Conference Paper
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%A Stubbemann, Lena
%A Dürrschnabel, Dominik
%A Refflinghaus, Robert
%B ACM Symposium on Eye Tracking Research and Applications
%D 2021
%I ACM
%R 10.1145/3448017.3457380
%T Neural Networks for Semantic Gaze Analysis in XR Settings
%U https://doi.org/10.1145%2F3448017.3457380 - BibTeXEndNote1.Dürrschnabel, D., Hanika, T., Stubbemann, M.: FCA2VEC: Embedding Techniques for Formal Concept Analysis. Presented at the (2021).
@inbook{durrschnabel2021fca2vec,
author = {Dürrschnabel, Dominik and Hanika, Tom and Stubbemann, Maximilian},
keywords = {2021 closure_operator fca2vec myown word2vec},
title = {FCA2VEC: Embedding Techniques for Formal Concept Analysis},
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}%0 Book Section
%1 durrschnabel2021fca2vec
%A Dürrschnabel, Dominik
%A Hanika, Tom
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%D 2021
%T FCA2VEC: Embedding Techniques for Formal Concept Analysis - BibTeXEndNote1.Koyda, M., Stumme, G.: Boolean Substructures in Formal Concept Analysis. ICFCA: International Conference on Formal Concept Analysis. pp. 38–53. Springer (2021).
@conference{koyda2021boolean,
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%I Springer
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%P 38-53
%T Boolean Substructures in Formal Concept Analysis
%@ 978-3-030-77866-8 - URLBibTeXEndNoteDOI1.Hanika, T., Hirth, J.: Quantifying the Conceptual Error in Dimensionality Reduction. In: Braun, T., Gehrke, M., Hanika, T., and Hernandez, N. (eds.) Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, ICCS 2021, Virtual Event, September 20-22, 2021, Proceedings. pp. 105–118. Springer (2021).
@inproceedings{DBLP:conf/iccs/HanikaH21,
author = {Hanika, Tom and Hirth, Johannes},
booktitle = {Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, ICCS 2021, Virtual Event, September 20-22, 2021, Proceedings},
editor = {Braun, Tanya and Gehrke, Marcel and Hanika, Tom and Hernandez, Nathalie},
keywords = {2021 itegpub kde kdepub myown scale-measure scaling},
pages = {105--118},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
title = {Quantifying the Conceptual Error in Dimensionality Reduction},
volume = 12879,
year = 2021
}%0 Conference Paper
%1 DBLP:conf/iccs/HanikaH21
%A Hanika, Tom
%A Hirth, Johannes
%B Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, ICCS 2021, Virtual Event, September 20-22, 2021, Proceedings
%D 2021
%E Braun, Tanya
%E Gehrke, Marcel
%E Hanika, Tom
%E Hernandez, Nathalie
%I Springer
%P 105--118
%R 10.1007/978-3-030-86982-3_8
%T Quantifying the Conceptual Error in Dimensionality Reduction
%U https://doi.org/10.1007/978-3-030-86982-3_8
%V 12879 - BibTeXEndNote1.Dürrschnabel, D., Stumme, G.: Force-Directed Layout of Order Diagrams Using Dimensional Reduction. In: Braud, A., Buzmakov, A., Hanika, T., and Le Ber, F. (eds.) Formal Concept Analysis. pp. 224–240. Springer International Publishing, Cham (2021).Order diagrams allow human analysts to understand and analyze structural properties of ordered data. While an expert can create easily readable order diagrams, the automatic generation of those remains a hard task. In this work, we adapt force-directed approaches, which are known to generate aesthetically-pleasing drawings of graphs, to the realm of order diagrams. Our algorithm ReDraw thereby embeds the order in a high dimension and then iteratively reduces the dimension until a two-dimensional drawing is achieved. To improve aesthetics, this reduction is equipped with two force-directed steps where one step optimizes the distances of nodes and the other one the distances of lines in order to satisfy a set of a priori fixed conditions. By respecting an invariant about the vertical position of the elements in each step of our algorithm we ensure that the resulting drawings satisfy all necessary properties of order diagrams. Finally, we present the results of a user study to demonstrate that our algorithm outperforms comparable approaches on drawings of lattices with a high degree of distributivity.
@inproceedings{10.1007/978-3-030-77867-5_14,
abstract = {Order diagrams allow human analysts to understand and analyze structural properties of ordered data. While an expert can create easily readable order diagrams, the automatic generation of those remains a hard task. In this work, we adapt force-directed approaches, which are known to generate aesthetically-pleasing drawings of graphs, to the realm of order diagrams. Our algorithm ReDraw thereby embeds the order in a high dimension and then iteratively reduces the dimension until a two-dimensional drawing is achieved. To improve aesthetics, this reduction is equipped with two force-directed steps where one step optimizes the distances of nodes and the other one the distances of lines in order to satisfy a set of a priori fixed conditions. By respecting an invariant about the vertical position of the elements in each step of our algorithm we ensure that the resulting drawings satisfy all necessary properties of order diagrams. Finally, we present the results of a user study to demonstrate that our algorithm outperforms comparable approaches on drawings of lattices with a high degree of distributivity.},
address = {Cham},
author = {Dürrschnabel, Dominik and Stumme, Gerd},
booktitle = {Formal Concept Analysis},
editor = {Braud, Agnès and Buzmakov, Aleksey and Hanika, Tom and Le Ber, Florence},
keywords = {2021 diagram_drawing itegpub lattices myown order_diagrams spring_embedder},
pages = {224--240},
publisher = {Springer International Publishing},
title = {Force-Directed Layout of Order Diagrams Using Dimensional Reduction},
year = 2021
}%0 Conference Paper
%1 10.1007/978-3-030-77867-5_14
%A Dürrschnabel, Dominik
%A Stumme, Gerd
%B Formal Concept Analysis
%C Cham
%D 2021
%E Braud, Agnès
%E Buzmakov, Aleksey
%E Hanika, Tom
%E Le Ber, Florence
%I Springer International Publishing
%P 224--240
%T Force-Directed Layout of Order Diagrams Using Dimensional Reduction
%X Order diagrams allow human analysts to understand and analyze structural properties of ordered data. While an expert can create easily readable order diagrams, the automatic generation of those remains a hard task. In this work, we adapt force-directed approaches, which are known to generate aesthetically-pleasing drawings of graphs, to the realm of order diagrams. Our algorithm ReDraw thereby embeds the order in a high dimension and then iteratively reduces the dimension until a two-dimensional drawing is achieved. To improve aesthetics, this reduction is equipped with two force-directed steps where one step optimizes the distances of nodes and the other one the distances of lines in order to satisfy a set of a priori fixed conditions. By respecting an invariant about the vertical position of the elements in each step of our algorithm we ensure that the resulting drawings satisfy all necessary properties of order diagrams. Finally, we present the results of a user study to demonstrate that our algorithm outperforms comparable approaches on drawings of lattices with a high degree of distributivity.
%@ 978-3-030-77867-5 - BibTeXEndNote1.Dürrschnabel, D., Koyda, M., Stumme, G.: Attribute Selection Using Contranominal Scales. In: Braun, T., Gehrke, M., Hanika, T., and Hernandez, N. (eds.) Graph-Based Representation and Reasoning. pp. 127–141. Springer International Publishing, Cham (2021).Formal Concept Analysis (FCA) allows to analyze binary data by deriving concepts and ordering them in lattices. One of the main goals of FCA is to enable humans to comprehend the information that is encapsulated in the data; however, the large size of concept lattices is a limiting factor for the feasibility of understanding the underlying structural properties. The size of such a lattice depends on the number of subcontexts in the corresponding formal context that are isomorphic to a contranominal scale of high dimension. In this work, we propose the algorithm ContraFinder that enables the computation of all contranominal scales of a given formal context. Leveraging this algorithm, we introduce \\($\$\)backslashdelta \\($\$\)\($\delta$\)-adjusting, a novel approach in order to decrease the number of contranominal scales in a formal context by the selection of an appropriate attribute subset. We demonstrate that \\($\$\)backslashdelta \\($\$\)\($\delta$\)-adjusting a context reduces the size of the hereby emerging sub-semilattice and that the implication set is restricted to meaningful implications. This is evaluated with respect to its associated knowledge by means of a classification task. Hence, our proposed technique strongly improves understandability while preserving important conceptual structures.
@inproceedings{10.1007/978-3-030-86982-3_10,
abstract = {Formal Concept Analysis (FCA) allows to analyze binary data by deriving concepts and ordering them in lattices. One of the main goals of FCA is to enable humans to comprehend the information that is encapsulated in the data; however, the large size of concept lattices is a limiting factor for the feasibility of understanding the underlying structural properties. The size of such a lattice depends on the number of subcontexts in the corresponding formal context that are isomorphic to a contranominal scale of high dimension. In this work, we propose the algorithm ContraFinder that enables the computation of all contranominal scales of a given formal context. Leveraging this algorithm, we introduce \\($\$\)backslashdelta \\($\$\)\($\delta$\)-adjusting, a novel approach in order to decrease the number of contranominal scales in a formal context by the selection of an appropriate attribute subset. We demonstrate that \\($\$\)backslashdelta \\($\$\)\($\delta$\)-adjusting a context reduces the size of the hereby emerging sub-semilattice and that the implication set is restricted to meaningful implications. This is evaluated with respect to its associated knowledge by means of a classification task. Hence, our proposed technique strongly improves understandability while preserving important conceptual structures.},
address = {Cham},
author = {Dürrschnabel, Dominik and Koyda, Maren and Stumme, Gerd},
booktitle = {Graph-Based Representation and Reasoning},
editor = {Braun, Tanya and Gehrke, Marcel and Hanika, Tom and Hernandez, Nathalie},
keywords = {2021 fca kde myown},
pages = {127--141},
publisher = {Springer International Publishing},
title = {Attribute Selection Using Contranominal Scales},
year = 2021
}%0 Conference Paper
%1 10.1007/978-3-030-86982-3_10
%A Dürrschnabel, Dominik
%A Koyda, Maren
%A Stumme, Gerd
%B Graph-Based Representation and Reasoning
%C Cham
%D 2021
%E Braun, Tanya
%E Gehrke, Marcel
%E Hanika, Tom
%E Hernandez, Nathalie
%I Springer International Publishing
%P 127--141
%T Attribute Selection Using Contranominal Scales
%X Formal Concept Analysis (FCA) allows to analyze binary data by deriving concepts and ordering them in lattices. One of the main goals of FCA is to enable humans to comprehend the information that is encapsulated in the data; however, the large size of concept lattices is a limiting factor for the feasibility of understanding the underlying structural properties. The size of such a lattice depends on the number of subcontexts in the corresponding formal context that are isomorphic to a contranominal scale of high dimension. In this work, we propose the algorithm ContraFinder that enables the computation of all contranominal scales of a given formal context. Leveraging this algorithm, we introduce \\($\$\)backslashdelta \\($\$\)\($\delta$\)-adjusting, a novel approach in order to decrease the number of contranominal scales in a formal context by the selection of an appropriate attribute subset. We demonstrate that \\($\$\)backslashdelta \\($\$\)\($\delta$\)-adjusting a context reduces the size of the hereby emerging sub-semilattice and that the implication set is restricted to meaningful implications. This is evaluated with respect to its associated knowledge by means of a classification task. Hence, our proposed technique strongly improves understandability while preserving important conceptual structures.
%@ 978-3-030-86982-3 - URLBibTeXEndNoteDOI1.Draude, C., Gruhl, C., Hornung, G., Kropf, J., Lamla, J., Leimeister, J.M., Sick, B., Stumme, G.: Social Machines. Informatik Spektrum. (2021).
@article{2021,
author = {Draude, Claude and Gruhl, Christian and Hornung, Gerrit and Kropf, Jonathan and Lamla, Jörn and Leimeister, Jan Marco and Sick, Bernhard and Stumme, Gerd},
journal = {Informatik Spektrum},
keywords = {2021 itegpub myown},
month = {nov},
title = {Social Machines},
year = 2021
}%0 Journal Article
%1 2021
%A Draude, Claude
%A Gruhl, Christian
%A Hornung, Gerrit
%A Kropf, Jonathan
%A Lamla, Jörn
%A Leimeister, Jan Marco
%A Sick, Bernhard
%A Stumme, Gerd
%D 2021
%J Informatik Spektrum
%R 10.1007/s00287-021-01421-4
%T Social Machines
%U https://doi.org/10.1007%2Fs00287-021-01421-4 - URLBibTeXEndNoteDOI1.Koopmann, T., Stubbemann, M., Kapa, M., Paris, M., Buenstorf, G., Hanika, T., Hotho, A., Jäschke, R., Stumme, G.: Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research. Scientometrics. (2021).Creation and exchange of knowledge depends on collaboration. Recent work has suggested that the emergence of collaboration frequently relies on geographic proximity. However, being co-located tends to be associated with other dimensions of proximity, such as social ties or a shared organizational environment. To account for such factors, multiple dimensions of proximity have been proposed, including cognitive, institutional, organizational, social and geographical proximity. Since they strongly interrelate, disentangling these dimensions and their respective impact on collaboration is challenging. To address this issue, we propose various methods for measuring different dimensions of proximity. We then present an approach to compare and rank them with respect to the extent to which they indicate co-publications and co-inventions. We adapt the HypTrails approach, which was originally developed to explain human navigation, to co-author and co-inventor graphs. We evaluate this approach on a subset of the German research community, specifically academic authors and inventors active in research on artificial intelligence (AI). We find that social proximity and cognitive proximity are more important for the emergence of collaboration than geographic proximity.
@article{koopmann2021proximity,
abstract = {Creation and exchange of knowledge depends on collaboration. Recent work has suggested that the emergence of collaboration frequently relies on geographic proximity. However, being co-located tends to be associated with other dimensions of proximity, such as social ties or a shared organizational environment. To account for such factors, multiple dimensions of proximity have been proposed, including cognitive, institutional, organizational, social and geographical proximity. Since they strongly interrelate, disentangling these dimensions and their respective impact on collaboration is challenging. To address this issue, we propose various methods for measuring different dimensions of proximity. We then present an approach to compare and rank them with respect to the extent to which they indicate co-publications and co-inventions. We adapt the HypTrails approach, which was originally developed to explain human navigation, to co-author and co-inventor graphs. We evaluate this approach on a subset of the German research community, specifically academic authors and inventors active in research on artificial intelligence (AI). We find that social proximity and cognitive proximity are more important for the emergence of collaboration than geographic proximity.},
author = {Koopmann, Tobias and Stubbemann, Maximilian and Kapa, Matthias and Paris, Michael and Buenstorf, Guido and Hanika, Tom and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
journal = {Scientometrics},
keywords = {2020 kdepub myown regio},
title = {Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research},
year = 2021
}%0 Journal Article
%1 koopmann2021proximity
%A Koopmann, Tobias
%A Stubbemann, Maximilian
%A Kapa, Matthias
%A Paris, Michael
%A Buenstorf, Guido
%A Hanika, Tom
%A Hotho, Andreas
%A Jäschke, Robert
%A Stumme, Gerd
%D 2021
%J Scientometrics
%R 10.1007/s11192-021-03922-1
%T Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research
%U https://doi.org/10.1007/s11192-021-03922-1
%X Creation and exchange of knowledge depends on collaboration. Recent work has suggested that the emergence of collaboration frequently relies on geographic proximity. However, being co-located tends to be associated with other dimensions of proximity, such as social ties or a shared organizational environment. To account for such factors, multiple dimensions of proximity have been proposed, including cognitive, institutional, organizational, social and geographical proximity. Since they strongly interrelate, disentangling these dimensions and their respective impact on collaboration is challenging. To address this issue, we propose various methods for measuring different dimensions of proximity. We then present an approach to compare and rank them with respect to the extent to which they indicate co-publications and co-inventions. We adapt the HypTrails approach, which was originally developed to explain human navigation, to co-author and co-inventor graphs. We evaluate this approach on a subset of the German research community, specifically academic authors and inventors active in research on artificial intelligence (AI). We find that social proximity and cognitive proximity are more important for the emergence of collaboration than geographic proximity. - URLBibTeXEndNote1.Schaefermeier, B., Stumme, G., Hanika, T.: Topological Indoor Mapping through WiFi Signals. (2021).The ubiquitous presence of WiFi access points and mobile devices capable of measuring WiFi signal strengths allow for real-world applications in indoor localization and mapping. In particular, no additional infrastructure is required. Previous approaches in this field were, however, often hindered by problems such as effortful map-building processes, changing environments and hardware differences. We tackle these problems focussing on topological maps. These represent discrete locations, such as rooms, and their relations, e.g., distances and transition frequencies. In our unsupervised method, we employ WiFi signal strength distributions, dimension reduction and clustering. It can be used in settings where users carry mobile devices and follow their normal routine. We aim for applications in short-lived indoor events such as conferences.
@article{schaefermeier2021topological,
abstract = {The ubiquitous presence of WiFi access points and mobile devices capable of measuring WiFi signal strengths allow for real-world applications in indoor localization and mapping. In particular, no additional infrastructure is required. Previous approaches in this field were, however, often hindered by problems such as effortful map-building processes, changing environments and hardware differences. We tackle these problems focussing on topological maps. These represent discrete locations, such as rooms, and their relations, e.g., distances and transition frequencies. In our unsupervised method, we employ WiFi signal strength distributions, dimension reduction and clustering. It can be used in settings where users carry mobile devices and follow their normal routine. We aim for applications in short-lived indoor events such as conferences.},
author = {Schaefermeier, Bastian and Stumme, Gerd and Hanika, Tom},
keywords = {mapping myown wifi},
note = {cite arxiv:2106.09789Comment: 18 pages},
title = {Topological Indoor Mapping through WiFi Signals},
year = 2021
}%0 Journal Article
%1 schaefermeier2021topological
%A Schaefermeier, Bastian
%A Stumme, Gerd
%A Hanika, Tom
%D 2021
%T Topological Indoor Mapping through WiFi Signals
%U http://arxiv.org/abs/2106.09789
%X The ubiquitous presence of WiFi access points and mobile devices capable of measuring WiFi signal strengths allow for real-world applications in indoor localization and mapping. In particular, no additional infrastructure is required. Previous approaches in this field were, however, often hindered by problems such as effortful map-building processes, changing environments and hardware differences. We tackle these problems focussing on topological maps. These represent discrete locations, such as rooms, and their relations, e.g., distances and transition frequencies. In our unsupervised method, we employ WiFi signal strength distributions, dimension reduction and clustering. It can be used in settings where users carry mobile devices and follow their normal routine. We aim for applications in short-lived indoor events such as conferences. - URLBibTeXEndNote1.Schäfermeier, B., Stumme, G., Hanika, T.: Towards Explainable Scientific Venue Recommendations, http://arxiv.org/abs/2109.11343, (2021).Selecting the best scientific venue (i.e., conference/journal) for the submission of a research article constitutes a multifaceted challenge. Important aspects to consider are the suitability of research topics, a venue's prestige, and the probability of acceptance. The selection problem is exacerbated through the continuous emergence of additional venues. Previously proposed approaches for supporting authors in this process rely on complex recommender systems, e.g., based on Word2Vec or TextCNN. These, however, often elude an explanation for their recommendations. In this work, we propose an unsophisticated method that advances the state-of-the-art in two aspects: First, we enhance the interpretability of recommendations through non-negative matrix factorization based topic models; Second, we surprisingly can obtain competitive recommendation performance while using simpler learning methods.
@misc{schafermeier2021towards,
abstract = {Selecting the best scientific venue (i.e., conference/journal) for the submission of a research article constitutes a multifaceted challenge. Important aspects to consider are the suitability of research topics, a venue's prestige, and the probability of acceptance. The selection problem is exacerbated through the continuous emergence of additional venues. Previously proposed approaches for supporting authors in this process rely on complex recommender systems, e.g., based on Word2Vec or TextCNN. These, however, often elude an explanation for their recommendations. In this work, we propose an unsophisticated method that advances the state-of-the-art in two aspects: First, we enhance the interpretability of recommendations through non-negative matrix factorization based topic models; Second, we surprisingly can obtain competitive recommendation performance while using simpler learning methods.},
author = {Schäfermeier, Bastian and Stumme, Gerd and Hanika, Tom},
keywords = {myown venue_recommendations},
note = {cite arxiv:2109.11343},
title = {Towards Explainable Scientific Venue Recommendations},
year = 2021
}%0 Generic
%1 schafermeier2021towards
%A Schäfermeier, Bastian
%A Stumme, Gerd
%A Hanika, Tom
%D 2021
%T Towards Explainable Scientific Venue Recommendations
%U http://arxiv.org/abs/2109.11343
%X Selecting the best scientific venue (i.e., conference/journal) for the submission of a research article constitutes a multifaceted challenge. Important aspects to consider are the suitability of research topics, a venue's prestige, and the probability of acceptance. The selection problem is exacerbated through the continuous emergence of additional venues. Previously proposed approaches for supporting authors in this process rely on complex recommender systems, e.g., based on Word2Vec or TextCNN. These, however, often elude an explanation for their recommendations. In this work, we propose an unsophisticated method that advances the state-of-the-art in two aspects: First, we enhance the interpretability of recommendations through non-negative matrix factorization based topic models; Second, we surprisingly can obtain competitive recommendation performance while using simpler learning methods. - URLBibTeXEndNote1.Stubbemann, M., Stumme, G.: LG4AV: Combining Language Models and Graph Neural Networks for Author Verification. (2021).
@article{stubbemann2021lg4av,
author = {Stubbemann, Maximilian and Stumme, Gerd},
keywords = {2021 itegpub l3spub myown regio},
title = {LG4AV: Combining Language Models and Graph Neural Networks for Author Verification},
year = 2021
}%0 Journal Article
%1 stubbemann2021lg4av
%A Stubbemann, Maximilian
%A Stumme, Gerd
%D 2021
%T LG4AV: Combining Language Models and Graph Neural Networks for Author Verification
%U https://arxiv.org/abs/2109.01479